{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data('mnist.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,)\n",
      "(10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 15 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "for i in range(15):\n",
    "    plt.subplot(3,5,i+1) # 绘制前15个手写体数字，以3行5列子图形式展示\n",
    "    plt.tight_layout() # 自动适配子图尺寸\n",
    "    plt.imshow(x_train[i], cmap='Greys') # 使用灰色显示像素灰度值\n",
    "    plt.title(\"Label: {}\".format(y_train[i])) # 设置标签为子图标题\n",
    "    plt.xticks([]) # 删除x轴标记\n",
    "    plt.yticks([]) # 删除y轴标记"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n         18,  18,  18, 126, 136, 175,  26, 166, 255, 247, 127,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,  30,  36,  94, 154, 170,\n        253, 253, 253, 253, 253, 225, 172, 253, 242, 195,  64,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,  49, 238, 253, 253, 253, 253,\n        253, 253, 253, 253, 251,  93,  82,  82,  56,  39,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,  18, 219, 253, 253, 253, 253,\n        253, 198, 182, 247, 241,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,  80, 156, 107, 253, 253,\n        205,  11,   0,  43, 154,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  14,   1, 154, 253,\n         90,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 139, 253,\n        190,   2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  11, 190,\n        253,  70,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  35,\n        241, 225, 160, 108,   1,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n         81, 240, 253, 253, 119,  25,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,  45, 186, 253, 253, 150,  27,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,  16,  93, 252, 253, 187,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0, 249, 253, 249,  64,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,  46, 130, 183, 253, 253, 207,   2,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  39,\n        148, 229, 253, 253, 253, 250, 182,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  24, 114, 221,\n        253, 253, 253, 253, 201,  78,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,  23,  66, 213, 253, 253,\n        253, 253, 198,  81,   2,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,  18, 171, 219, 253, 253, 253, 253,\n        195,  80,   9,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,  55, 172, 226, 253, 253, 253, 253, 244, 133,\n         11,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0, 136, 253, 253, 253, 212, 135, 132,  16,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0],\n       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0]], dtype=uint8)"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "tensorflow112",
   "language": "python",
   "display_name": "tensorflow112"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}